Differential impact of environmental factors on airborne live bacteria and inorganic particles in an underground walkway

We previously reported that variations in the number and type of bacteria found in public spaces are influenced by environmental factors. However, based on field survey data alone, whether the dynamics of bacteria in the air change as a result of a single environmental factor or multiple factors working together remains unclear. To address this, mathematical modeling may be applied. We therefore conducted a reanalysis of the previously acquired data using principal component analysis (PCA) in conjunction with a generalized linear model (Glm2) and a statistical analysis of variance (ANOVA) test employing the χ2 distribution. The data used for the analysis were reused from a previous public environmental survey conducted at 8:00–20:00 on May 2, June 1, and July 5, 2016 (regular sampling) and at 5:50–7:50 and 20:15–24:15 on July 17, 2017 (baseline sampling) in the Sapporo underground walking space, a 520-meter-long underground walkway. The dataset consisted of 60 samples (22 samples for “bacterial flora”), including variables such as “temperature (T),” “humidity (H),” “atmospheric pressure (A),” “traffic pedestrians (TP),” “number of inorganic particles (Δ5: 1–5 μm),” “number of live airborne bacteria,” and “bacterial flora.” Our PCA with these environmental factors (T, H, A, and TP) revealed that the 60 samples could be categorized into four groups (G1 to G4), primarily based on variations in PC1 [Loadings: T(˗0.62), H(˗0.647), TP(0.399), A(0.196)] and PC2 [Loadings: A(˗0.825), TP(0.501), H(0.209), T(˗0.155)]. Notably, the number of inorganic particles significantly increased from G4 to G1, but the count of live bacteria was highest in G2, with no other clear pattern. Further analysis with Glm2 indicated that changes in inorganic particles could largely be explained by two variables (H/TP), while live bacteria levels were influenced by all explanatory variables (TP/A/H/T). ANOVA tests confirmed that inorganic particles and live bacteria were influenced by different factors. Moreover, there were minimal changes in bacterial flora observed among the groups (G1–G4). In conclusion, our findings suggest that the dynamics of live bacteria in the underground walkway differ from those of inorganic particles and are regulated in a complex manner by multiple environmental factors. This discovery may contribute to improving public health in urban settings.

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Abstract
We previously reported variations in the number and type of bacteria found in public spaces, specifically in the Sapporo Underground Pedestrian Space, a 520-meter underground walkway.These variations were influenced by environmental factors, as reported by our study.To gain a better understanding of how environmental factors impact the quantity of airborne bacteria, we conducted a reanalysis of the previously acquired data using principal component analysis (PCA) in conjunction with a generalized linear model (Glm2) and a statistical analysis of variance (ANOVA) test employing the χ 2 distribution.The dataset consisted of 60 samples (22 samples for "bacterial flora"), including variables such as "temperature (T)", "humidity (H)", "atmospheric pressure (A)", "traffic pedestrians (TP)", "number of inorganic particles (Δ5: 1-5 µm)", "number of live airborne bacteria", and "bacterial flora".Our PCA with these environmental factors (T, H, A, and TP) revealed that the 60 samples could be categorized into four groups (G1 to G4), primarily based on variations in PC1 [Loadings: T(˗0.62),H(˗0.647),TP(0.399),A(0.196)] and PC2 [Loadings: A(˗0.825),TP(0.501),H(0.209), T(˗0.155)].Notably, the number of inorganic particles significantly increased from G4 to G1, but the count of live bacteria was highest in G2, with no other clear pattern.Further analysis with Glm2 indicated that changes in inorganic particles could largely be explained by two variables (H/TP), while live bacteria levels were influenced by all explanatory variables (TP/A/H/T).ANOVA tests confirmed that inorganic particles and live bacteria were influenced by different factors.Moreover, there were minimal changes in bacterial flora observed among the groups (G1-G4).In conclusion, our findings suggest that the dynamics of live bacteria in the underground walkway differ from those of inorganic particles and are regulated in a complex manner by multiple environmental factors.This discovery may contribute to improving public health in urban settings.
Keywords: airborne live bacteria, built environments, Sapporo underground pedestrian space, principal component analysis, generalized linear model

Introduction
Humans spend over 90% of their time indoors, in places such as homes, offices, schools, hospitals, and public areas [1][2][3].Therefore, keeping these spaces clean and free from air pollution is essential.Additionally, humans emit a significant number of bacteria through activities such as coughing, sneezing, talking, and breathing, contributing to indoor airborne bacteria.This can impact health by spreading or worsening infectious diseases [4][5][6][7][8][9][10].Researchers have monitored microbial communities in various indoor public places to understand their dynamics [11], but our understanding of the influence of factors such as temperature, humidity, atmospheric pressure, traffic pedestrians, and inorganic particles on bacteria levels in these spaces is limited.
To address this, we previously investigated the impact of walker occupancy combined with other factors (temperature, humidity, atmospheric pressure, dust particles) on airborne bacterial features [colony forming units (CFUs) and operational taxonomic units (OTUs)] in the Sapporo Underground Pedestrian Space in Sapporo, Japan (https://www.sapporo-chikamichi.jp/) [12].The results were interesting and revealed a positive relationship between walker occupancy and airborne bacteria that changed with increased temperature and humidity, and these findings had implications for improving public health in urban communities [12].It has further been revealed that multiple environmental factors influence the dynamics of airborne bacteria in a complex manner, but whether this effect is based on one specific factor or multiple factors working together is unclear.
On the basis of recent research, the influence of individual environmental factors on airborne bacteria is becoming increasingly evident.For example, in a hospital room maintained at constant temperature and humidity (25°C and 55%), similar types of fungi and bacteria were identified over at least 3 days from air samples [13].Furthermore, the study of airborne bacteria in patient rooms showed that the diversity and composition of the indoor bacterial communities changed readily in response to variations in ventilation or temperature [14].Other studies have shown that in the air in public spaces, the number of dust particles with attached bacteria is significantly influenced by walker occupancy [15][16][17].Our previous study revealed that the dynamics of airborne bacteria (mainly derived from soil) in outdoor spaces can significantly change depending on humidity, rainfall, wind speed, and/or sunlight [18].However, few studies have comprehensively and accurately simulated the various environmental factors that influence the dynamics of airborne bacteria.
In this study, we conducted a reanalysis of the previously acquired dataset [12] using principal component analysis (PCA) in conjunction with a generalized linear model (Glm2) and performed a statistical ANOVA test employing the χ 2 distribution.

Dataset reused from our previous study and research flows
The dataset used in this study is a continuation of our previous research [12], including 60 samples (22 samples for "bacterial flora") and variables such as "temperature (T)", "humidity (H)", "atmospheric pressure (A)", "traffic pedestrians (TP)", "number of inorganic particles (Δ5: 1-5 µm)", "number of live airborne bacteria" (Table S1), and "bacterial flora" (Tables S2); samples were collected on May 2, June 1, and July 5, 2016 (8:00 h to 20:00 h), and July 15, 2017 (5:50 h to 7:50 h / 22:15 h to 24:45 h).The analysis, as depicted in Fig. 1, involved several steps.First, the dataset (T, H, A, and TP) was subjected to PCA of group variables (Fig. 1, right flow).Subsequently, differences in viable bacterial counts and Δ5 between groups were compared.Then, Glm2 analysis, which is an R package based on "Poisson distribution", was employed to assess the degree of fit for environmental factors explaining variations in the number of bacteria and particles between groups (Fig. 1, middle flow).The validity of the fitting was confirmed through a statistical ANOVA test employing the χ 2 distribution.In addition, variation in the bacterial flora dataset was visualized using PCA (Fig. 1, left flow).Further details for each analysis method are provided below.

PC performance and analysis software
The

PCA
Two analyses were performed, as shown in Fig. 1 (see above).In particular, the commands below were used to determine the three values ("Standard Deviation," "Proportion of Variance," "Cumulative Proportion").The allocation rate [Principal component (PC) 1 and 2] from the "Cumulative Proportion" to the XY axis was calculated.The degree of factors contributing to PC1 and PC2 was calculated using the command "summary."Furthermore, the contribution rate for each data point was visualized using the following commands: >data <-read.csv("Dataname.csv")# "data name" is a temporary placeholder.

>data >result <-princomp(data, cor = TRUE) >summary(result, loadings = TRUE) >plot(result$scores[, 1] ~ result$scores[, 2]) >result$scores
In the provided R code, the temporary name "Data name.csv" was read into the variable "data."PCA was then performed using the "princomp" function, and a summary of the results, including the loadings, was displayed.The scores were plotted, showing the contribution rate for each data point.

Glm2
As mentioned above, to assess the degree of fit for environmental factors explaining variations in the number of bacteria and particles between datasets, Glm2 was run with the following commands: were displayed, and the log likelihood for each model was calculated.Finally, a comparison of the models was performed with the "fit_f" variable.From these calculations, the values of "Deviance," "LogLik," and "AIC (Akaike Information Criterion)" were obtained.

Validation of fitting
The validation for the degree of fitting was conducted by ANOVA with χ 2 using the following codes: A p-value of 0.05 or less was considered statistically significant.

Other statistics
Comparisons among groups were performed by a Bonferroni-Dunn test.A p-value of < 0.05 was considered statistically significant.
Notably, each group corresponded to a specific collection date: "2016 May 2" for G1, "2016 June 1" for G2, "2016 July 5" for G3, and "2017 July 15" for G4.Next, we compared the numbers of viable bacteria and particles between groups.While the number of inorganic particles significantly increased from G4 to G1 (Fig. 3A), the count of live bacteria was highest in G2, with no other clear pattern (Fig. 3B).The results from these graphs were consistent with findings from previous reports involving daily comparisons [12, see "Fig.3B and Fig. 4A" in the text].However, in our earlier studies, we did not explicitly confirm that these variations were attributable to a combination of specific environmental factors.Thus, it is now evident that the count of viable bacteria and inorganic particles suspended in the underground walkway varies in response to distinct changes in environmental factors.Next, we employed Glm2 to assess the degree of fit for environmental factors explaining variations in bacterial and particle counts between datasets.Three key indicators ("Residual Deviance," "AIC," and "logLik") were calculated and compared in two scenarios: "vs null"

The degree of fit to
(no consideration given to environmental factors) and "vs full" (considering all environmental factors).In our analysis, we aimed to identify the smallest combination of environmental factors that closely approximated the fitting value of "full."Remarkably, the three indicators consistently changed in a similar manner, underscoring the accuracy of this model.In terms of the dynamics of particles, as compared with the values of "full," a rapid decrease to the level of "null" was seen in "T" and "H," and the combined value of the two factors ("H"+"A" and "H"+"TP") almost matched the "vs full" value.This result indicated that the dynamics of particles can be easily explained by simple factors alone (Fig. 4).By contrast, when examining the dynamics of airborne live bacteria, the situation is more complex.The indicator values gradually decrease to the "null" level, indicating that to understand the dynamics of live bacteria, almost all environmental factors should be considered (Fig. 5).We therefore verified its validity through statistical processing using ANOVA with χ 2 distribution.As expected, it was found that the dynamics of fine particles

Changes in bacterial flora are minimal among the four groups (G1-G4) divided by PCA with environmental factors
To explore the potential influence of changes in bacterial flora on the results, PCA was conducted using bacterial flora data from previous studies (Table S2) [12].Then, the plots were compared among the four groups distinguished by the impact of environmental factors.
The number of OTUs, representing the bacterial load, peaked in G4, suggesting an increase in bacterial abundance from early spring to summer (Fig. 7A).However, no discernible alterations in bacterial flora were noted between the groups (Fig. 7B and C).These results suggested minimal changes in bacterial flora among the four groups (G1-G4), as identified by PCA based on environmental factors.

Discussion
We visualized the dynamics of environmental factors using PCA with the previous datasets obtained from the underground walking space [12], and subsequently examined the impact of these dynamics on suspended particles and airborne live bacteria by fitting Glm2 with ANOVA using χ 2 .Notably, our analysis revealed that the dynamics of airborne live bacteria in the underground walkway exhibit distinct mathematical patterns compared with those of inorganic particles.Specifically, in contrast to particles, the dynamics of live bacteria are intricately regulated by multiple environmental factors.
Using PCA, the dynamics of four environmental factors were compressed in two dimensions, and the dataset clearly divided into four groups (G1-G4) based on the sampling date.Temperature and humidity predominantly influenced the X-axis variation, shifting from G1 to G4 as the sampling date moved from May to July, reflecting seasonal changes.Atmospheric pressure and the number of people passing by were the primary contributors to Y-axis changes.Notably, G2, characterized by decreased atmospheric pressure and light rain, occupied a distinct position from other groups with fair weather [12].Thus, the PCA plot effectively captured seasonal variations with the flow of pedestrians, clearly delineating the dataset into the four groups reflecting the distinct weighting of environmental factors.
To verify the PCA results, we used a fitting model (Glm2) with ANOVA using χ 2 .This model, which follows a Poisson distribution, allows for an optimal probability distribution when the data are discrete values with no linear variations and the upper limit is unpredictable [19].In other words, the model allows the probability distribution of environmental factors to be visualized for each combination to explain the changes in the number of bacteria and particles, and their differences can be easily determined by comparing three indicators ("Deviance," "LogLik," and "AIC").Moreover, the degree of fitting for each combination of predictors (environmental factors) can be simply calculated for significance using an approximate calculation method (ANOVA using χ 2 ).
As expected, PCA revealed that fine particles varied significantly in value between the groups (G1-G4).Specifically, the number of fine particles (Δ5: 1-5 µm) with values >10 5    gradually decreased from G4 to G1.This change may be related to the fact that the temperature and humidity of this underground walking space increased from spring to summer, and this finding was consistent with those of other studies [20][21][22].Because the data for G4 comprised the least number of pedestrians late at night and early in the morning, the quantity of suspended particles in the space is also affected by the number of pedestrians.In fact, studies have reported that the number of people present in elevators or subway stations influences the dynamics of particles suspended in indoor environments [23,24].
Interestingly, PCA and a comparative analysis among the four visualized groups suggested that the number of particles suspended in underground walking spaces varies depending on temperature, humidity, and the number of pedestrians.To verify this, we compared the degree of fitting using the Glm2 model.As expected, it was clear that although the dynamics of particles can be simply explained by temperature and humidity alone, they are more comprehensively explained by a combination of humidity and the number of pedestrians.Thus, these results suggest that the fluctuations in the number of particles suspended in this underground walking space can be explained by two environmental factors, namely humidity and the number of pedestrians.
By contrast, the dynamics of the number of airborne live bacteria in the underground walking space showed a completely different pattern from that of fine particles.Specifically, PCA with comparative analysis among the four groups (G1-G4) clearly showed that the number of live bacteria significantly increased into G2.This finding suggests that the dynamics of live bacteria were dependent on a certain temperature and humidity with an increasing number of pedestrians and decreasing atmospheric pressure.Similarly, the model analysis of Glm2 with χ 2 revealed that the dynamics could not be explained by specific environmental factors or simple combinations.Thus, it is apparent that all of the environmental factors considered in this study are required to explain the dynamics of live airborne bacteria in the underground space.
It is unclear why environmental factors affecting the dynamics of particles in underground walkways differ from those influencing the count of live bacteria.Notably, there was a significant difference in the total number of airborne particles versus bacteria, with particles showing 10,000 times higher values.Because most airborne bacteria adhere to some form of inorganic fine particles [25,26], this implies that the proportion of fine particles with bacteria attached is small.In other words, although studies are being conducted on the relationship between the number of fine particles and the number of airborne bacteria in public environments [27][28][29], particle count alone may not be an appropriate indicator of the number of airborne bacteria in public spaces.
The explanation as to why the number of airborne live bacteria in the underground space significantly increased under such limited environmental conditions remains unclear; however, the following observations may provide some insight.First, the survival of bacteria, such as Escherichia coli or Staphylococcus aureus, on dry surfaces is enhanced in low temperature and low humidity environments [30].Second, the survivability of bacteria attached to a dry surface heated to the level of human skin (~37C) was significantly lower than when the surface was not heated [31,32].Third, given that no change in bacterial flora was observed between the groups (G1-G4), the increase in the total number of bacteria itself has an impact.Thus, moderate temperature and humidity may have a significant impact on the survival of airborne bacteria attached to particles.
This study, conducted in the underground walking space in Sapporo, has some limitations.
First, to ensure the universality of these results, similar studies in other public environments are crucial.Second, the count of live bacteria was surprisingly small compared with bacterial flora analysis results.This suggests that additional investigation is needed into the validity of the culture method and the potential presence of "viable but non-culturable" bacteria.Third, although environmental factors were visualized using a fitting model, it is essential to verify whether the new measurements align with the expected probability distribution.
In summary, we find that the dynamics of airborne live bacteria in the underground walkway differ from those of particles and are intricately regulated by multiple environmental factors.This study stands out as one of the few to mathematically and statistically visualize the environmental factors influencing the number of airborne bacteria in public spaces, contributing to the enhancement of public health in urban settings.The area surrounded by the dotted line at the top shows the contents of the dataset used, including 60 samples (22 samples for "bacterial flora") and variables with "temperature (T)", "humidity (H)", "atmospheric pressure (A)", "traffic pedestrians (TP)", "number of inorganic particles (Δ5: 1-5 µm)", "number of live airborne bacteria", and "bacterial flora" (see Tables S1 and S2) [12].Right flow shows the protocol for grouping the relationship between environmental factors and the number of bacteria/particles with PCA.Middle flow shows the protocol of Glm2 fitting with ANOVA using χ 2 .Left flow shows the protocol for grouping bacterial flora with PCA.shows a comparison of airborne live bacteria in the underground space between the groups (G1-G4).Right table shows the results of statistical analysis.See above (Fig. 3A).
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Yamaguchi et al 14 can
be explained in full by only two variables (H+TP) (Fig.6A, see "Red color"), whereas the dynamics of live bacterial counts could not be explained in full by less than the complete set of variables (Fig.6B, see "Red color").These results clearly indicate that the dynamics of particles and live bacteria are influenced by different environmental factors.

Fig. 1 .
Fig. 1.Flowchart showing the research flow for the analysis

Fig. 3 .
Fig. 3. Airborne live bacteria and particles in the underground space showed different

Fig. 4 .
Fig. 4. Degree of fitting to Glm2 revealed that the dynamics of particles can be simply

Fig. 5 .
Fig. 5. Degree of fitting to Glm2 revealed that the dynamics of airborne live bacteria